Book Image

Journey to Become a Google Cloud Machine Learning Engineer

By : Dr. Logan Song
Book Image

Journey to Become a Google Cloud Machine Learning Engineer

By: Dr. Logan Song

Overview of this book

This book aims to provide a study guide to learn and master machine learning in Google Cloud: to build a broad and strong knowledge base, train hands-on skills, and get certified as a Google Cloud Machine Learning Engineer. The book is for someone who has the basic Google Cloud Platform (GCP) knowledge and skills, and basic Python programming skills, and wants to learn machine learning in GCP to take their next step toward becoming a Google Cloud Certified Machine Learning professional. The book starts by laying the foundations of Google Cloud Platform and Python programming, followed the by building blocks of machine learning, then focusing on machine learning in Google Cloud, and finally ends the studying for the Google Cloud Machine Learning certification by integrating all the knowledge and skills together. The book is based on the graduate courses the author has been teaching at the University of Texas at Dallas. When going through the chapters, the reader is expected to study the concepts, complete the exercises, understand and practice the labs in the appendices, and study each exam question thoroughly. Then, at the end of the learning journey, you can expect to harvest the knowledge, skills, and a certificate.
Table of Contents (23 chapters)
1
Part 1: Starting with GCP and Python
4
Part 2: Introducing Machine Learning
8
Part 3: Mastering ML in GCP
13
Part 4: Accomplishing GCP ML Certification
15
Part 5: Appendices
Appendix 2: Practicing Using the Python Data Libraries

Preparing the platform

While data input has a big impact on model quality, the hardware/software platform where we train/validate/test the model will also impact the model and the development process. Choosing the right platform is very important to the ML process.

While certainly, you can choose to use a desktop or laptop for ML model development, it is a recommended practice to use cloud platforms, thanks to the great advantages that cloud computing provides: self-provisioning, on-demand, resilience, and scalability, at a global scale. Many tools are provided in cloud computing to assist data scientists in data preparation and model development.

Among the cloud service providers, Google Cloud Platform provides great ML platforms to data scientists: flexible, resilient, and performant, from end to end. We will discuss more details of the Google Cloud ML platform in the third part of the book.

Now that we have prepared the datasets and ML platform, let’s dive right...